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Protecting Your Corporation from Counterparty Loss
5 April 2016
Featured Speakers
2
Mehna Raissi
Senior Director
Enterprise Risk Solutions
Moody’s Analytics
Charles Dafler
Assistant Director
Credit Solutions Specialist
Moody’s Analytics
Agenda
» Market Outlook & Trends
» Identifying Common Challenges in Credit Risk Management
» Bridging the Credit Risk Gap in Your Organization
» Applying Effective Credit Models and Examples
» Q&A
3
Market Outlook & Trends
Economic Volatility
5Source: Moody’s Chief Economist, John Lonski
» Actual corporate default rate (blue series) is at its highest level since 2010
» Predicted default rate (Moody’s Analytics Public Firm Expected Default Frequency)
showing continued deterioration
Banks are tightening their credit
» Moody’s Analytics proprietary Credit Research Database (CRD™) shows that banks’
commercial lending default have begun to rise in U.S.
» Banks will respond by tightening credit
» Firms will have a harder time borrowing and their risk will permeate to their counterparties
6
Rolling 12-month private firm default rate by type including near-defaults
Common Challenges in Credit Risk Management
What credit risk challenge(s) keeps you up at night?
Data
Quality &
Availability
Comprehensive
Assessment
Strong
Model
Standardized
Process
Different
Approaches
TechnologyUnforeseen Issues
Ongoing
Monitoring
Systematic
Framework
Organization
Challenges or
Changes
Industry
ChallengesGlobal Risk
Polling Question #1
Where are the risks associated with counterparties?
10
Counterparty Risk
Trading Risk
Risk Deterioration
Vendor Risk
Risk-based Pricing
Underwriting Risk
Limit Setting
What are the consequences of credit risk?
11
Bad Debt &
Loss of Income
Disruption to
Supply Chain
Miscalculation of
Capital Reserves
Unforeseen
Damages
The Process: Assessing Counterparty Credit Risk
12
Evaluate
potential
customer
Set credit limits
and terms
Monitor
exposures
Determine
credit score
Perform sector
analysis
Ideal Analysis
Common Challenges in Corporate Credit Risk Management
Data Quality & Availability
What is the data
quality?
How is the data
captured?
Standardized Processes
Ongoing Monitoring
Other Risk Drivers
Credit Risk Models
How to minimize
errors?
Are credit policies
systematic and
consistent?
What are the most
effective credit risk
tools?
Are you using the
best model?
How to manage
potential
counterparty risk?
What early warning
indicators highlight
risk deterioration?
What other factors
should be taken into
consideration?
What represents a
comprehensive
analysis?
Bridging the Gap in Your Organization
Is there disconnect between Sales and Credit Risk?
Potential Challenges:
• What is the point of this?
• This is a roadblock! We are going to lose
this sale!
• Why didn’t this get approved?
• I know this customer - override!
• I have been doing this for a longtime- I
don’t need a model!
Establishing a Common Language:
• Accuracy – education on purpose of the
model and scoring tool – no black boxes!
• Efficiency - Collective knowledge of
process and the common goal of
minimizing potential loses
• Transparency - Understanding key risk
factors that drive business and the
approval process
• Consistency - Gathering the qualitative
and quantitative factors upfront as part of
the pre-qualification process
15
16
The purpose of credit scores
A Master Rating Scale helps ensure the interpretation of risk is consistent
» Across the firm (front to back office) globally
» Across segments (portfolios)
» Over time as underwriters and analysts change
» Provides a good distribution for credit risk
They are used in a common and consistent language across the firm – a Master Rating Scale
0%
5%
10%
15%
20%
25%
30%
35%
Aaa Aa A Baa Ba B Caa Ca C D
suppliers
wholesale
retail
Rating Scale
Pe
rce
nt o
f o
bse
rva
tio
ns
Maximizing the value of credit scores
» Pre-qualification
» Deal approval
» Exposure Loss Estimation
» Risk Monitoring
» Risk-based pricing
» Limit Setting
» Reserve Estimation
» Benchmarking
» Peer Comparison
Zero Limits
Low Limits
Medium
Limits
High Limits
Aaa
D
Ba
C
Caa
Ca
B
Baa
Score
Aa
A
Underwriting Limit SettingPricing
17
Attributes of an EffectiveCredit Framework
Key Requirements for an Effective Credit Risk Framework
19
Risk Models
Risk Analysis
Peer Analysis
Early Warning
Monitoring
Reporting
» Consistency
» Efficiency
» Transparency
» Accuracy
Able to distinguish defaulters from non-defaulters (i.e., “action” in the underlying
data sample)
Clear, objective, and uniformly understood
Capable of being assessed in a reasonable timeframe using accessible,
consistently available data
Possessing unique information value (i.e., non-duplicative, non-correlated)
Supported by intuition and general business sense
Measurable and verifiable (using historical data at some point in future)
Checking the boxes for a good Credit Risk Model
Characteristics of Good Candidate Risk Factors
20
Counterparty Credit Risk Models
Common types of credit risk models available
21
Evaluate potential customer
Set credit limits and terms
Monitor exposures
Determinecredit score
Perform sector analysis
Lack of peer, industry
and regional insight
Ineffectiv e risk
monitoring
Insufficient data on
public & priv ate firms
Absence of a
standardized process
Common Challenges
Ideal Analysis
Market-driven(point in time)
PROS:
-Forward looking
-Very reactive
-Very predictive
-Wide coverage
CONS:
-Volatile
-requires external
data
Financial statement-driven
PROS:
-transparent
-consistent
-intuitive
CONS:
-backward looking
-updated only with
new statements
Credit Agency Ratings(through the cycle)
PROS:
-thorough
-widely
understood
-long track record
CONS:
-lagging indicator
-labor intensive
-subjective
A good counterparty credit risk solutions utilizes the best aspects of all available approaches
22
Evaluate potential customer
Set credit limits and terms
Monitor exposures
Determinecredit score
Perform sector analysis
Lack of peer, industry
and regional insight
Ineffectiv e risk
monitoring
Insufficient data on
public & priv ate firms
Absence of a
standardized process
Common Challenges
Typical Analysis
Counterparty Credit Risk Models
Market-driven(point in time)
PROS:
-Forward looking
-Very reactive
-Very predictive
-Wide coverage
CONS:
-Volatile
-requires external
data
Financial statement-driven
PROS:
-transparent
-consistent
-intuitive
CONS:
-backward looking
-updated only with
new statements
Credit Agency Ratings(through the cycle)
PROS:
-thorough
-widely
understood
-long track record
CONS:
-lagging indicator
-labor intensive
-subjective
-for rated firms
Underwrite with
consistent and
transparent model
Benchmark to agency’s
through-the-cycle credit
view
Monitor risk exposure
with forward-looking
market based metric
Case Study
Case Study: Sabine and Forest Oil merger
What we knew in 2014…
Sabine Oil and Gas
» Privately held (market-driven model won’t work)
Forest Oil
» Publically traded [NYSE:FST] (market-based model available)
Merger announced in May 2014
» Sabine announced plans to acquire Forest Oil in mid 2014
Then…
Sabine Oil & Gas Corp files for bankruptcy in July 2015
24
Sabine Oilfinancial statement assessmentbenchmark to agency rating
25
Using RiskCalc econometric model
and YE2013 financials we calculate
Sabine has 8.46% default probability
YE2014 financials show
11.32% default probability,
implied rating in C category
Source: RiskCalc and Moody’s.com
Forest Oilmarket-based model has quick reaction to credit riska leading indicator of downgrades and default
26
Pro
babili
ty o
f defa
ult (
log s
cale
) M
oody’s
ratin
g
Merger
announcedDefault
Source: CreditEdge
Polling Question Two
What type of credit model do you use?
• External model – rating agency
• External model – market based
• External model – econometric-based
• Internal model – expert judgment driven model
• Internal model – quantitative model
• More than one answer above
• Other
27
Polling Question #2
Putting a Credit Model into Practice
30
EL = PD x LGD x EADExpected Loss Probability of
DefaultLoss Given Default
Exposure at Default
… how likely you are to go into default
… how much am I likely to lose once you go into default
… and what you’re still going to owe me when you go into default
Probability of Default
Loss Given Default
Exposure at Default
= x x3% 30¢
on the dollar
$5MMof the $10MM
I originally lent youlikelihood
which means:
When I lend you money, the amount of money I could potentially lose depends on three things …
Expected Loss
$45K
What does a comprehensive credit risk model do?
It helps measure what you stand to lose with default and recovery risk measures.
1. How many scorecards?
Accuracy,
Stability and
Consistency
Efficiency/
Maintenance
MORE LESS
Flexible, Easy to
Manage, Cost Effective
2. How customized?
Leveraged
and Tailored
Low
High
Degree of
Customization
Standardized,
Off the Shelf
Fully
Customized
Cost Effective, Quick
Delivery, Easy to Deploy
What’s the right scorecard balance for your organization?
Purely Judgmental Purely Empirical
3. Modeling Approach?
EXPERT
JUDGEMENT
HYBRID QUANTITATIVE
Statistically driven
Expert opinion input
31
Desired end-state: a scorecard which blends empirically-derived risk measures with expert judgment
Qualitative Overlay
Quantitative PD%
Quantitative Model
Qualitative Score (0-100)
Fin
al
Outp
ut
Rating-Implied PD
Borrower Rating
Total Score
Example Quantitative Factors
Liquidity
Profitability
Debt Service Coverage
Leverage
………
Example Qualitative Factors
Market Share
Diversification
Mgmt Experience
Supplier Pressure
……..
32
65% weight 35% weight
Quantitative modeling explainedO
bse
rve
d d
efa
ult
rate
Low High
Low
High
Liquidity Ratio
Ob
se
rve
d d
efa
ult
rate
Low High
Low
High
Leverage
Each level of a ratio is associated with a different default rate, and their weights are chosen to maximize the fit between predicted default rate and observed default rate in the database
Liquidity Example: Firm A’s liquidity is worse than Firm B’s, below the median and above the median, respectively.
However, the empirical evidence shows both firms have above-median default risk based on liquidity alone.
Another example: Firm A and Firm B have above-median leverage, but both map to much different default risk
based on leverage alone
All relevant ratios must be weighed together in the final model construction
Our human intuition can be misled – empirical evidence can overcome this issue
Firm BFirm A BA
» Validation is the process of rendering a statistically derived conclusion about the usefulness and reliability of a scorecard
» Validation makes use of historical data to determine whether or not the scorecard is robust
» Validation answers important questions about the accuracy and stability of the scorecard as a decision making tool
What does
validation
involve?
Why is
validation
important?
» Validation ensures that the scorecards are at least as good as an industry benchmark
» Regulators increasingly expect it – this trend is expected to continue and expand to more and more industries
» Validation can also help ensure that strong borrowers are not turned away – and weak borrowers are not extended credit
It is important to test the model’s accuracy and stability through validation
34
» Assume you have a portfolio of 100 counterparties
» All were rated 1 year ago
» Since then 10 of them defaulted
» How did the model rank-order the counterparties?
35
Model Accuracy Explained
Perfect Model - Unattainable
Good Model
Random Model - Worthless
Polling Question Three
What topics discussed today resonated the most and will be your top area of focus?
• Improving the credit scoring models being used
• Enhancing your overall credit risk framework
• Educating your internal stakeholders on the importance of credit scoring
• More than one answer above
• Other
36
Polling Question #3
38
Recap: Credit Risk Management Best Practices
GranularityIncreases the power to diversify the risk between similar credits
Ongoing Monitoring & Early Warning SignalDetects credit deterioration by combining relevant data and rank orders risk well
Assessment of Risk Drivers Relative contributions and sensitivity measures provide an understanding of the risk drivers by providing transparency
BenchmarkingBenchmark an obligor to the sample pool and/or other firms in the portfolio or peer groups by industry and asset size
ComprehensivenessAll encompassing qualitative, probability of default, recovery analytics solution that can be accessed across your organization
Extensive sample pool of dataComprehensive asset class data including financial statements and defaults from Moody’s Analytics Credit Research Database
TransparencyDocumented approach, clear methodology, consistent inputs and outputs
Empirically ValidatedSufficient data to separate development, validation samples and ongoing model performance
Accuracy ImportanceModel has good “power”, high quality of credit ratings differentiation
Forward LookingAccounts for effects of Credit Cycle by Industry and Market Performance
Questions?
Slides from today’s presentation and supplemental material can be
downloaded from the “Resources” tab of this presentation console.
The recording will also be sent following this webinar.
moodysanalytics.com
Charles Dafler
Assistant Director, Credit Solution [email protected]
Mehna Raissi
Senior Director, Product [email protected]
40
APPENDIXExamples of Risk Rating Models
RiskCalc – Financial Statement Driven Model withForward Looking Credit Cycle Adjustment
RiskCalc data source: the Credit Research Database
43
.
RiskCalc Determines PD from Credit Ratios and Credit Cycle
44
Ratio drivers point out many weaknesses in firm’s financials
Compares borrowers against peer group for additional transparency
Incorporates qualitative factors in credit assessment
Qualitative factors focused on industry/market (customer power), management (experience in industry), company (years in relationship) and balance sheet factors (audit method)
CreditEdge – Public Firm PD Model
One-Year Expected Default Frequency (EDF™) Measures
CreditEdge determines PD Based on Forward-Looking Market Valuations
CreditEdge Excel Add-in – Risk Dashboard
50
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